Metadata-Version: 2.1
Name: bitclust
Version: 0.0.9
Summary: Fast and memory-efficient clustering of long Molecular Dynamics
Home-page: https://github.com/rglez/bitclust
Author: Roy González-Alemán
Author-email: rglez.developer@gmail.com
License: UNKNOWN
Description: # BitClust: Fast and memory-efficient clustering of long Molecular Dynamics
        
        
        # Home Page
        -----------
        
        BitClust´s latest documentation is available [here](https://bitclust.readthedocs.io/en/latest/) 
        
        
        # Description
        -------------
        
        **BitClust** is a Python command-line interface (CLI) conceived for fast
        clustering of relatively long Molecular Dynamics trajectories following
        Daura's algorithm [1]. Retrieved clusters are roughly equivalent to those
        reported by **VMD's** internal command **measure cluster** but they are computed in a much faster way (see benchmark section for more details).
        
        
        # Motivation
        
        Nowadays very long simulations are carried on routinely. Enhanced sampling
        methods like metadynamics, REMD, and accelerated dynamics allow escaping from
        potential energy minima, returning trajectories that are conformationally sparsed
        and where every cluster can be potentially important to detect and analyze. Improvements
        on software designed to address this task is an important field of research.
        
        **BitClust** offer is a classical tradeoff; RAM for speed. It can
        calculate all pairwise distances between frames to run a clustering job and
        then store them in memory instead of recalculating them whenever a cluster is found.
        It is worth noting that used memory has been deeply optimized by encoding similarity distances
        as bits (0 if the distance is less equal than a specified threshold, 1 otherwise).
        This encoding result in a storage reduction as high as 32X compared to similar algorithms
        that saves the same information as single-precision float values.
        
        
        # Main Dependencies
        
        **BitClust** is built on the shoulders of two giants:
        
         *  [MDTraj software](http://mdtraj.org/1.9.0/)  that allows a very fast
            calculation of RMSD pairwise distances between all frames of trajectories in
            a parallelized fashion **and**
        
         * [bitarray third-party python library](https://pypi.org/project/bitarray/) 
           which offers a memory-efficient data structure of bit-vectors (bit arrays)
           and a set of bitwise operations that are the very heart of our clustering
           implementation.
        
        
        # Citation
        
        If you make use of **BitClust** in your scientific work, **BitCool** and [cite it ;)](https://doi.org/10.1021/acs.jcim.9b00828)
        
        
        # Licence
        
        **BitClust** is licensed under GNU General Public License v3.0.
          
          
        # Reference
        
        [1] Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236–240.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
